Cognitoware.Robotics.dll
Class RandomConditional<X, Y>
X: The type whose probability depends on Y.
Y: The type that affects the probability of X.
System.Object
Cognitoware.Mathematics.Probability.RandomConditional<X, Y>
Summary
The abstract base class for a conditional probability distribution.
Constructor Summary
Method Summary
Applies Bayes' Rule to the conditional distribution using a specific value of X.
Applies Bayes' Rule to the conditional distribution using a specific value of X
and attempts to convert the mean to a specific implementation of RandomDistribution.
Calculates the probability of a specific value result given a specific value range.
Creates a probability distribution over X conditioned on a specific value of Y.
Equals(Object)
Inherited from System.Object
Finalize()
Inherited from System.Object
GetHashCode()
Inherited from System.Object
GetType()
Inherited from System.Object
Creates a likelihood function for a specific value of result.
Creates a probability distribution of X given a random distribution across Y.
MemberwiseClone()
Inherited from System.Object
ToString()
Inherited from System.Object
Details
Calculates the probability of X given Y.
This is represented by the equation P(X|Y).
This class does not provide a representation of the condition distribution.
Classes like ConditionalMap and LikelihoodMap provide specific data structures that are useful in different circumstances.
Constructor Details
family RandomConditional()
Method Details
public virtual RandomDistribution<Y> BayesianInference(X data, RandomDistribution<Y> prior)
Applies Bayes' Rule to the conditional distribution using a specific value of X.
Parameters:
prior - The prior belief of Y.
data - The observed value of X.
Returns:
The probability distribution of Y conditioned on the prior value and the observed data.
public virtual RandomDistribution<Y> BayesianInference(X data, RandomDistribution<Y> prior)
Applies Bayes' Rule to the conditional distribution using a specific value of X
and attempts to convert the mean to a specific implementation of RandomDistribution.
Parameters:
prior - The prior belief of Y.
data - The observed value of X.
Returns:
The probability distribution of Y conditioned on the prior value and the observed data.
public virtual Double ConditionalProbabilityOf(X x, Y y)
Calculates the probability of a specific value result given a specific value range.
Parameters:
x - The value whose probability is being calculated.
y - The value that affects the probability of result.
Returns:
The conditional probability of result given range.
public virtual RandomDistribution<X> ConditionBy(Y y)
Creates a probability distribution over X given a known value y.
Parameters:
y - The value of Y over which the conditional distribution.
Returns:
The probability of X given the specific value of Y.
public virtual RandomDistribution<Y> LikelihoodOf(X x)
Creates a likelihood function for a specific value of result.
Parameters:
x - A conditional value in X.
Returns:
The likelihood of observing values in Y give the value of result.
public virtual RandomDistribution<X> Marginalize(RandomDistribution<Y> y)
Creates a probability distribution of X given a random distribution across Y.
The probability of a value of X in this distribution is equal to the average conditional
probability of X given Y (defined by this distribution) weighted by the probability
of each corresponding value Y in the parameter distribution.
Parameters:
y -
The distribuiton across Y that is used to weight the probability distributions of X.
Returns:
The expectation of X given the probable values of Y.